{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/mort1/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time, datetime\n",
    "\n",
    "import nibabel as nib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.ndimage.interpolation import zoom\n",
    "\n",
    "from nitorch.data import load_nifti\n",
    "from settings import settings\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binary brain mask used to cut out the skull.\n",
    "mask = load_nifti(settings[\"binary_brain_mask\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set random state seed\n",
    "r = 43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_table = \"PATH/TO/YOUR/MODIFIED/ADNI_MERGE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the data table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sometimes pre-processing fails and we removed\n",
    "# failed pre-processing in 067_S_0077/Screening \n",
    "\n",
    "failed_idx = list(df.loc[(df[\"PTID\"]==\"067_S_0077\") & (df[\"Visit\"] == \"Screening\")].index)\n",
    "df = df.drop(index=failed_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove all MCI subjects\n",
    "df = df[df['DX'] != 'MCI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_df_stats(df, df_train, df_val, df_test):\n",
    "    \"\"\"Print some statistics about the patients and images in a dataset.\"\"\"\n",
    "    headers = ['Images', '-> AD', '-> CN', 'Patients', '-> AD', '-> CN']\n",
    "\n",
    "    def get_stats(df):\n",
    "        df_ad = df[df['DX'] == 'Dementia']\n",
    "        df_cn = df[df['DX'] == 'CN']\n",
    "        return [len(df), len(df_ad), len(df_cn), len(df['PTID'].unique()), len(df_ad['PTID'].unique()), len(df_cn['PTID'].unique())]\n",
    "\n",
    "    stats = []\n",
    "    stats.append(['All'] + get_stats(df))\n",
    "    stats.append(['Train'] + get_stats(df_train))\n",
    "    stats.append(['Val'] + get_stats(df_val))\n",
    "    stats.append(['Test'] + get_stats(df_test))\n",
    "\n",
    "    print(tabulate(stats, headers=headers))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Images    -> AD    -> CN    Patients    -> AD    -> CN\n",
      "-----  --------  -------  -------  ----------  -------  -------\n",
      "All         969      475      494         344      193      151\n",
      "Train       697      360      337         248      145      103\n",
      "Val         100       40       60          36       18       18\n",
      "Test        172       75       97          60       30       30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Patient-wise train-test-split.\n",
    "# Select a number of subjects for each class, put all their images in the test set \n",
    "# and all other images in the train set. This is the split that is used in the paper to produce the heatmaps.\n",
    "test_subjects_per_class = 30\n",
    "val_subjects_per_class = 18\n",
    "\n",
    "subjects_AD = df[df['DX'] == 'Dementia']['PTID'].unique()\n",
    "subjects_CN = df[df['DX'] == 'CN']['PTID'].unique()\n",
    "subjects_CN = [p for p in subjects_CN if p not in subjects_AD]  # subjects that have both a CN and an AD scan should belong to the AD group\n",
    "\n",
    "subjects_AD_train, subjects_AD_test = train_test_split(subjects_AD, test_size=test_subjects_per_class, random_state=r)\n",
    "subjects_AD_train, subjects_AD_val = train_test_split(subjects_AD_train, test_size=val_subjects_per_class, random_state=r)\n",
    "subjects_CN_train, subjects_CN_test = train_test_split(subjects_CN, test_size=test_subjects_per_class, random_state=r)\n",
    "subjects_CN_train, subjects_CN_val = train_test_split(subjects_CN_train, test_size=val_subjects_per_class, random_state=r)\n",
    "\n",
    "subjects_train = np.concatenate([subjects_AD_train, subjects_CN_train])\n",
    "subjects_val = np.concatenate([subjects_AD_val, subjects_CN_val])\n",
    "subjects_test = np.concatenate([subjects_AD_test, subjects_CN_test])\n",
    "\n",
    "# Compile train and val dfs based on subjects.\n",
    "df_train = df[df.apply(lambda row: row['PTID'] in subjects_train, axis=1)]\n",
    "df_val = df[df.apply(lambda row: row['PTID'] in subjects_val, axis=1)]\n",
    "df_test = df[df.apply(lambda row: row['PTID'] in subjects_test, axis=1)]\n",
    "\n",
    "print_df_stats(df, df_train, df_val, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load images in matrix\n",
    "def create_dataset(dataset, z_factor, mask=None):\n",
    "    data_matrix = [] \n",
    "    labels = [] \n",
    "    for idx, row in dataset.iterrows():\n",
    "        path = row[\"filepath\"]\n",
    "        struct_arr = np.NAN\n",
    "        scan = nib.load(path)\n",
    "        struct_arr = scan.get_data().astype(np.float32)\n",
    "        if mask is not None:\n",
    "            struct_arr *= mask\n",
    "        if z_factor is not None:\n",
    "            struct_arr = zoom(struct_arr, z_factor)\n",
    "        data_matrix.append(struct_arr)\n",
    "        labels.append((row[\"DX\"] == \"Dementia\") *1)      \n",
    "    return np.array(data_matrix), np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z_factor = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting at Fri May 24 14:48:48 2019\n",
      "Train dataset..\n",
      "Time elapsed: 0:15:50.849694\n",
      "Validation dataset..\n",
      "Time elapsed: 0:17:59.846478\n",
      "Holdout dataset..\n",
      "Runtime: 0:21:22.336500\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting at \" + time.ctime())\n",
    "start = time.time()\n",
    "\n",
    "print(\"Train dataset..\")\n",
    "train_dataset, train_labels = create_dataset(df_train, z_factor=z_factor, mask=mask)\n",
    "print(\"Time elapsed: \" + str(datetime.timedelta(seconds=(time.time()-start))))\n",
    "print(\"Validation dataset..\")\n",
    "val_dataset, val_labels = create_dataset(df_val, z_factor=z_factor, mask=mask)\n",
    "print(\"Time elapsed: \" + str(datetime.timedelta(seconds=(time.time()-start))))\n",
    "print(\"Holdout dataset..\")\n",
    "holdout_dataset, holdout_labels = create_dataset(df_test, z_factor=z_factor, mask=mask)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Runtime: \" + str(datetime.timedelta(seconds=(end-start))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(697, 193, 229, 193)\n",
      "(100, 193, 229, 193)\n",
      "(172, 193, 229, 193)\n"
     ]
    }
   ],
   "source": [
    "print(train_dataset.shape)\n",
    "print(val_dataset.shape)\n",
    "print(holdout_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mask[:,:,50], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.imshow(train_dataset[-1][:,:,50], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.imshow(holdout_dataset[6][:,:,48], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5 = h5py.File(settings[\"train_h5\"], 'w')\n",
    "h5.create_dataset('X', data=train_dataset, compression='gzip', compression_opts=9)\n",
    "h5.create_dataset('y', data=train_labels, compression='gzip', compression_opts=9)\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5 = h5py.File(settings[\"val_h5\"], 'w')\n",
    "h5.create_dataset('X', data=val_dataset, compression='gzip', compression_opts=9)\n",
    "h5.create_dataset('y', data=val_labels, compression='gzip', compression_opts=9)\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5 = h5py.File(settings[\"holdout_h5\"], 'w')\n",
    "h5.create_dataset('X', data=holdout_dataset, compression='gzip', compression_opts=9)\n",
    "h5.create_dataset('y', data=holdout_labels, compression='gzip', compression_opts=9)\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (mort1)",
   "language": "python",
   "name": "mort1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
